app.py

import os
import uuid
import json
from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from fastapi.requests import Request
import pandas as pd
from google import genai
from google.genai import types

# -----------------------------
# FastAPI setup
# -----------------------------
app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

# -----------------------------
# Gemini client setup
# -----------------------------
client = genai.Client(api_key="AIzaSyB1jgGCuzg7ELPwNEEwaluQZoZhxhgLmAs")

UPLOAD_DIR = "tmp/uploads"
os.makedirs(UPLOAD_DIR, exist_ok=True)

# -----------------------------
# Helper functions
# -----------------------------
def get_metadata(df):
    return {
        "columns": list(df.columns),
        "dtypes": df.dtypes.apply(str).to_dict(),
        "null_counts": df.isnull().sum().to_dict(),
        "unique_counts": df.nunique().to_dict(),
        "sample_rows": df.head(3).to_dict(orient="records")
    }

def generate_metadata_analysis(metadata):
    metadata_text = str(metadata)
    model = "gemini-2.5-flash-lite"

    contents = [
        types.Content(
            role="user",
            parts=[types.Part.from_text(
                text=f"Analyze the following structured data metadata:\n{metadata_text}"
            )],
        ),
    ]

    generate_content_config = types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=0),
        response_mime_type="application/json",
        system_instruction=[types.Part.from_text(text="""You are a structured data analysis AI. 
1️⃣ Summary: concise description of data, assumptions
2️⃣ Suggestions: up to 3 actionable analyses/visualizations
Output must be strict JSON: {"Summary": "<short summary>", "Suggestion": ["<analysis #1>", "<analysis #2>", "<analysis #3>"]}
""")],
    )

    output_text = ""
    for chunk in client.models.generate_content_stream(
        model=model,
        contents=contents,
        config=generate_content_config,
    ):
        output_text += chunk.text

    return json.loads(output_text)

def generate_visualization(command, file_path):
    system_prompt_text = f"""
You are a Python assistant that MUST return output strictly in JSON format and NOTHING else.
The top-level JSON MUST contain exactly three keys in this order: "type", "code", "explanation".
Requirements:
- "type": visualization type ("bar", "pie", "line", etc.)
- "code": Python code as a string that prints numeric JSON to stdout. Use this for data access: df = pd.read_excel(r"{file_path}")
- "explanation": one-sentence description
"""
    MODEL = "gemini-2.5-flash-lite"
    contents = [types.Content(role="user", parts=[types.Part.from_text(text=command)])]

    generate_content_config = types.GenerateContentConfig(
        thinking_config=types.ThinkingConfig(thinking_budget=0),
        response_mime_type="application/json",
        system_instruction=[types.Part.from_text(text=system_prompt_text)],
    )

    output = ""
    for chunk in client.models.generate_content_stream(
        model=MODEL,
        contents=contents,
        config=generate_content_config,
    ):
        output += chunk.text

    return json.loads(output)

# -----------------------------
# Routes
# -----------------------------
@app.get("/", response_class=HTMLResponse)
def home(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

@app.post("/upload", response_class=JSONResponse)
async def upload_excel(file: UploadFile = File(...)):
    file_ext = os.path.splitext(file.filename)[1]
    file_id = str(uuid.uuid4())
    file_path = os.path.join(UPLOAD_DIR, f"{file_id}{file_ext}")

    with open(file_path, "wb") as f:
        f.write(await file.read())

    df = pd.read_excel(file_path)
    metadata = get_metadata(df)
    analysis = generate_metadata_analysis(metadata)

    # Store session info temporarily
    session_data = {
        "file_path": file_path,
        "metadata": metadata,
        "analysis": analysis
    }

    return JSONResponse(session_data)

@app.post("/generate_plot", response_class=JSONResponse)
async def generate_plot(command: str = Form(...), file_path: str = Form(...)):
    visualization_json = generate_visualization(command, file_path)
    return JSONResponse(visualization_json)